Disrupting networks of hate
Characterising Hateful Networks and Removing Critical Nodes
Bibliographic Data
| ID | 4697298 |
|---|---|
| Authors | Wafa Alorainy (0000-0002-1342-0317, Shaqra University, corresponding author), Pete Burnap (0000-0003-0396-633X, Cardiff University, corresponding author), Han Liu (0000-0003-1868-9312, Shenzhen University, corresponding author), Matthew Williams (0000-0003-0892-0998), Matthew L Williams (0000-0003-2566-6063, Cardiff University, corresponding author), L Giommoni (0000-0002-3127-654X, Cardiff University, corresponding author) |
| Year | 2022 |
| Volume | 12 |
| Issue | 1 |
| Publication date | 2022-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-021-00818-z |
| OpenAlex | W3216275653 |
| Language | EN |
| Citations received | 4 |
| References cited | 80 |
Hateful individuals and groups have increasingly been using the Internet to express their ideas, spread their beliefs and recruit new members. Understanding the network characteristics of these hateful groups could help understand individuals' exposure to hate and derive intervention strategies to mitigate the dangers of such networks by disrupting communications. This article analyses two hateful followers' networks and three hateful retweet networks of Twitter users who post content subsequently classified by human annotators as containing hateful content. Our analysis shows similar connectivity characteristics between the hateful followers networks and likewise between the hateful retweet networks. The study shows that the hateful networks exhibit higher connectivity characteristics when compared to other "risky" networks, which can be seen as a risk in terms of the likelihood of exposure to, and propagation of, online hate. Three network performance metrics are used to quantify the hateful content exposure and contagion: giant component (GC) size, density and average shortest path. In order to efficiently identify nodes whose removal reduced the flow of hate in a network, we propose a range of structured node-removal strategies and test their effectiveness. Results show that removing users with a high degree is most effective in reducing the hateful followers network connectivity (GC, size and density), and therefore reducing the risk of exposure to cyberhate and stemming its propagation
Internet privacy · The Internet · World Wide Web · Computer Science · Engineering · Hate Speech and Cyberbullying Detection · Opinion Dynamics and Social Influence · Social Media and Politics
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The Relative Ineffectiveness of Criminal Network Disruption
Error and attack tolerance of complex networks
Sentiment strength detection in short informal text
Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality
Islamophobia and Twitter
Graphs over time
Graph evolution
Social Networks and the Performance of Individuals and Groups.
Statistical mechanics of complex networks
Hate Online
Everyone's an influencer
An Integrated Threat Theory of Prejudice
Bowling Alone
The Strength of Weak Ties
Naive Learning in Social Networks
Reciprocity
Valence-based homophily on Twitter
Studying Hate Crime with the Internet
Factoring and weighting approaches to status scores and clique identification
A faster algorithm for betweenness centrality
Tweeting the terror
Characterizing the Twitter network of prominent politicians and SPLC-defined hate groups in the 2016 US presidential election
Robustness of social and web graphs to node removal
Persuasive Storytelling by Hate Groups Online
Social Network Analysis
Suicidal Disclosures among Friends
| Unique citing works | 4 |
|---|---|
| Citations per year | 1 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |